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This success breeds competition among streaming providers to increase customer acquisition and retention by creating music recommendation algorithms that have just the right ingredients to serve up the tunes their customers crave.

Why is this? I personally believe it is because of the nature of the recommendations on Pandora. Pandora only allows users to create playlists based on one artist or song, which often doesn’t encapsulate the original mood the listener was looking for and yields poor suggestions that often are repeated after as little as 10 songs later. On the other end of the streaming service spectrum are platforms like 8 tracks, which do suggest playlists that a user may be interested in listening, but all of these playlists have to be manually created by users. Services like 8 tracks have more dynamic playlists than Pandora, but again these services rely on users to actively create playlists and then suggesting the correct playlists to users. Somewhere in the middle of the human editorial and algorithmically generated playlist spectrum of music streaming services is Spotify. This compromise is one of the main reasons Spotify has the largest market share as of March 2016.

Spotify uses a combination of collaborative filtering and other playlist statistics to suggest very poignant songs to users that feel like that music guru friend you trust is making the suggestion. Spotify's collaborative filtering works by analyzing a users' playlist and finding other users' playlists that have the same songs, but maybe a a few more, and then will suggest those additional songs to the original user. Spotify not only uses this method for song suggestion, but also weights songs based on whether a user favorited it and listened to it many times following the initial like or even when a user is suggested a song and skips it within the first minute. Because of this combination of collaborative filtering and certain statistics tracking, Spotify can suggest extremely accurate song recommendations that feel eerily familiar. As great as Spotify's recommendation engine is, what if there was way to build upon its already impressive algorithm and to suggest songs that are even more playlist specific.

In early 2014, Spotify acquired EchoNest; the industry’s leading music intelligence company, providing developers with the deepest understanding of music content and music fans. Through the use of Spotify's API, which now allows access to these EchoNest services, developers can provide seed information and target attributes in a query and the API will send back recommended songs in its response. This EchoNest API endpoint is the backbone of many suggestive services like Shazam, Pandora etc.., As you can probably imagine, choosing the best query parameters is thus very important and indeed this is what determines the accuracy and relevance os the  recommendations the API responds with. As stated previously, Muse optimizes these query parameters to be as representative of an entire playlist as possible and therefore bring back recommendations that are more relevant. So how does Muse do it?

Upon login via Spotify oAuth, users automatically see their local iTunes playlists and Spotify playlists on the left-hand column. If for some reason, their iTunes Library XML file is not at a standard location on their computer, users can specify the correct path for Muse to look for the file.

From here, a user can click on a playlist, which is where the magic of Muse takes place through the following algorithmic process which I have appended a diagram below followed by bullet points to explain each step in greater detail:

• Upon choosing a playlist, Muse will send each song within the playlist to Spotify's API to get back the attributes of the song including tempo, danceability, energy etc..

• Muse will then multiply the song attribute object by the play count for that song. For instance, if song A has a play count of 30 and song B a play count of 10, there will be 29 duplicates of the attribute object for song A and 9 duplicates of the attribute object for song B going into step 3.

• Muse then finds the weighted average of these songs to create the Phantom Average Track, which is an attribute object that best represents the composition or mood of the entire playlist.

• Muse then uses the artists of the five songs with the shortest Euclidian distances to the Phantom Average Track as seed artists as well the Phantom Average Tracks's attributes as target attributes to send to Spotify''s API to find recommended songs.

• The recommended songs sent back from Spotify are filtered to remove any duplicates with the original playlist and then sorted by relevance as defined by the Euclidean distance of the song's attributes from the original playlist's Phantom Average Track's attributes

Once the Muse algorithm has completed, users can then click on any recommended song. The chosen song's title and artist name will then be used to query YouTube's API and return and then play the top related video. Users can also click the play button which will play both playlist tracks and recommendations in order by relevance score.

Users can then drag recommendations they like into their playlist, which changes the composition of their playlist thus triggering the Muse algorithm to rerun and get new relevance scores and recommendations based on the playlist with the newly added song.

The application of this recursive playlist generation is very interesting for its potential to create varied suggestions through a unique decision path. For example, if the user wanted to to expand a current playlist by 10 songs that were as similar to the original playlist as possible by specifying a low recommendation relevance, then Muse would add the top recommendation with each recursive iteration and approach a playlist attribute variance minimum as fast as possible until the playlist duration or number of intended songs was achieved.

Yet, if a low enough recommendation relevance was set by the user (say 70%), then with each recursive iteration, Muse would add the recommendation with a relevance score closest to 70% which would mean that playlist generation may take a slower, more circuitous route towards the playlist attribute variance minimum.

The best way to visualize this process is to imagine falling down a whirlpool. The initial width of whirlpool may be wide, similar to a playlist you may have containing songs with varying attributes, but as you fall further into the whirlpool, its width tightens and the playlist's variance of song attributes approaches a minimum. It is important to note though, that like how a whirlpool twists and turns away from the center as it approaches its minimum, so too does the variability of recommended songs with each recursive step meaning that each added recommendation brings you closer to the bottom of the whirlpool, but may pull you in different directions throughout its descent depending on the variability of the recommendations. Finally, the pace at which you are pulled down the whirlpool creating a tighter playlist variance is defined by the recommendation relevance level initially set in the playlist generation form.

https://gist.github.com/sjstebbins/943b895931bba566e10b1aa79798a315

• Set up unique user accounts using Firebase so as to only require initial playlist seeding by Spotify and iTunes once and to store additional metrics to improve the algorithm including skipping of songs and colloborative filtering
• Create the form mentioned previously to recursively generate entire playlists given user specifications
• Deploy as Beta application
• Compared recommendation success to other streaming services

Muse was created using:

• Python Flask backend
• Spotify API for music attirbutes and recommendations
• YouTube API for video suggestions
• Material-UI and Jquery UI for front-end design
• Selenium for web scraping